This post is part of a series of articles discussing game monetization concepts and strategies in Core. The goal of this series is to help creators improve the monetization in their games by sharing knowledge and information, as well as learning from the experiences of the Core community. If you would like to contribute your own article, please contact the author.
In game development, analytics refer to the process of analyzing player and game data in order to gain insights and information, which can then be used to make improvements to the game. The goal of this article is to define some of the key metrics and terms used in game analytics to give Core game developers an understanding of the terms and how to use them.
Core provides both Creator analytics and Perk analytics in the Creator Portal. Creator analytics provide information about your game, such as play count and retention. Perk analytics provide information about each of your Perks, such as the quantity sold. Not all the metrics described below are provided by Core, but many can be calculated on your own.
The following metrics are associated with retention.
Daily Active Users defines how many unique users played the game within a single day.
DAU = number of unique players in a day
This provides information on how popular your game is each day. The higher the DAU, the more players are playing your game.
Monthly Active Users defines how many unique users played the game at least once within the last month.
MAU = number of unique players in a month
This information is useful to determine how many of your monthly users are daily users, which indicates how well your game retains players. If most of your monthly users play the game daily, then your game has great retention.
This can be measured by calculating the Stickiness Rate, which is just the ratio of daily to monthly active users.
Stickiness Rate = DAU / MAU
The higher the rate, the more users play your game daily.
The retention rate is one of the most important metrics that tells you how many players are returning to play your game. The RR measures how many players are retained and are still playing your game after a certain period of time.
RR = number of players returning / number of players total
For example, if 100 users played your game for the first time today, and tomorrow 50 of these users play again, then the retention rate for 1 day is 50%. If 10 of these users are still playing your game after 7 days, then the retention rate for 7 days is 10%.
Common intervals to measure retention are listed below, although any interval can be used.
D1 - Day 1 Retention
Measures what percent of players are still playing your game the next day.
D7 - Day 7 Retention
Measures what percent of players are still playing your game after one week.
D30 - Day 30 Retention
Measures what percent of players are still playing your game after one month.
Average industry numbers may be in the neighborhood of 25% for D1, 6% for D7, and 1.5% for D30, depending on the genre, platform, and many other factors. Since the numbers may be quite different on the Core platform, you will want to do your own research.
The session count is how many times a user plays your game per day. For example, if a user plays your game once in the morning, once in the afternoon, and once at night, then the session count is 3 for that day.
session count = number of times a user plays the game in a day
The higher the session count the better. A high session count means your game is compelling enough that users want to come back frequently to play it.
The session length is the amount of time a user spends playing your game during a session.
session length = amount of time a user plays the game during a session
For example, if a player starts playing your game at 1:00 pm, and ends playing at 1:15 pm, then the session length is 15 minutes. This is often calculated as an average, taking into account the average session length of many players.
The longer the session length the better. Long session times mean your players enjoy playing and spending time in your game. The session length varies dramatically based on the type of game, so any comparisons should be made against similar games.
The following metrics are associated with monetization.
The average revenue per user measures how much revenue you’re generating for each player. This gives you an idea of how much the average player spends over a certain time period.
ARPU = revenue / number of players
For example, if your game’s monthly revenue is $2000, and you have 1000 players during that month, then the ARPU for that month is equal to $2000/1000 = $2. In other words, one active player brings you an average of $2 per month.
Common time intervals to measure this metric are listed below.
- ARPDAU - Day
- ARPWAU - Week
- ARPMAU - Month
Note that both paying and non-paying users are taken into account. The ARPU is an indicator of the effectiveness of the monetization. The higher it is, the more money a player brings for the time period. This can also be used to measure the impact of changes in game monetization. If the ARPU increases after a change, then you know you did things right.
The average revenue per paying user takes into account paying players only, instead of all players as was the case above.
ARPPU = revenue / number of paying players
For example, if your game’s monthly revenue is $2000, and you have 20 paying players, then the ARPPU for that month is equal to $2000/20 = $100. So one paying player brings you an average of $100 per month.
This metric shows how much a loyal paying user is willing to pay, and their reaction to prices in your game and their value. The goal is to make paying users like what they pay for, so that they are willing to pay again.
The lifetime value is the predicted amount a player will spend in your game during the entire customer lifetime.
LTV = ARPU x Customer Lifetime
For example, if the average revenue per user is $20 per month, and the user plays your game for 10 months, then the LTV = $20 x 10 = $200.
The lifetime value can be used to target your best customers, or determine which products provide the most revenue. LTV can also help you set your budget for acquiring new players. If the average cost of getting a new paying player is below the LTV, then you will be making a profit in the long run.
The average transaction value is the average amount of money a player spends on a transaction.
ATV = revenue / number of transactions
For example, if your game generates $500 in a month, and you sold 100 items that month, then the ATV for the month = $500/100 = $5. A player spends an average of $5 per transaction during that month.
The ATV is often used to evaluate monetization strategies and product pricing. The goal is often to increase the ATV so that the game generates more revenue per player.
The conversion rate measures how long it takes for a player to decide to make their first or next purchase.
conversion rate = time it takes a player to decide to make a purchase
Converting players into buyers is very important in game monetization since once a player makes that first purchase, they are more likely to buy more in the future.
The following metrics are associated with the specific items sold in the game.
As with any store, you will want to keep track of the sales and revenue each item generates. Evaluate various price points to determine the ideal price of each item.
Keep track of sales and revenue by product category to understand which types of products your players want to pay for.
With all these metrics, you are probably wondering what numerical values are above average, average, or below average for your game. You can look online for industry averages, but the numbers may be different on the Core platform. Therefore, it is recommended that you contact a Core staff member knowledgeable about current analytics to give you an idea of the average numbers.
Please join the discussion and let us know about your ideas or experiences in Core related to this topic.